Diversity and technology—Challenges for the next decade in personnel selection
Bibliographic record
Abstract
The world of work is changing quickly.An aging workforce, largescale population migration, the democratization of remote work following the COVID-19 pandemic, and technological advancements, currently most famously driven by advances in artificial intelligence (AI), are just some of the driving forces of the changing world of work.Research is needed to properly understand those changes and their potential consequences on job seekers, employees, organizations, and the society more broadly.This Special Issue brings together a series of six original and insightful articles, based on research that was initially presented at the 6th small group meeting of the European Network of Selection Researchers (ENESER) organized by Martin Kleinmann and Annika Wilhelmy in Zurich in 2021.Together, these articles span two topics central to the changing world of work: Diversity and Technology.In this introductory article, we emphasize the growing importance of those two topics in relation to personnel recruitment, selection, or assessment.We then provide a brief overview of the six articles, and highlight how they contribute to enhancing our understanding of the role that diversity and technology play in various stages of the hiring process, from job applications to selection decisions.We conclude by providing suggestions about opportunities for future research in recruitment and selection that continues examining diversity and technology as well as their increasingly important overlaps.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.059 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".